A Generic Workflow for the Data FAIRification Process

The FAIR guiding principles aim to enhance the Findability,Accessibility,Interoperability and Reusability of digital resources such as data,for both humans and machines.The process of making data FAIR(“FAIRification”)can be described in multiple steps.In this paper,we describe a generic step-by-step FAIRification workflow to be performed in a multidisciplinary team guided by FAIR data stewards.The FAIRification workflow should be applicable to any type of data and has been developed and used for“Bring Your Own Data”(BYOD)workshops,as well as for the FAIRification of e.g.,rare diseases resources.The steps are:1)identify the FAIRification objective,2)analyze data,3)analyze metadata,4)define semantic model for data(4a)and metadata(4b),5)make data(5a)and metadata(5b)linkable,6)host FAIR data,and 7)assess FAIR data.For each step we describe how the data are processed,what expertise is required,which procedures and tools can be used,and which FAIR principles they relate to.

Expanding Non-Patient COVID-19 Data: Towards the FAIRification of Migrants' Data in Tunisia, Libya and Niger

This article describes the FAIRification process(which involves making data Findable, Accessible, Interoperable and Reusable—or FAIR—for both machines and humans) for data related to the impact of COVID-19 on migrants, refugees and asylum seekers in Tunisia, Libya and Niger, according to the scheme adopted by GO FAIR. This process was divided into three phases: pre-FAIRification, FAIRification and postFAIRification. Each phase consisted of seven steps. In the first phase, 118 in-depth interviews and 565 press articles and research reports were collected by students and researchers at the University of Sousse in Tunisia and researchers in Niger. These interviews, articles and reports constitute the dataset for this research. In the second phase, the data were sorted and converted into a machine actionable format and published on a FAIR Data Point hosted at the University of Sousse. In the third phase, an assessment of the implementation of the FAIR Guidelines was undertaken. Certain barriers and challenges were faced in this process and solutions were found. For FAIR data curation, certain changes need to be made to the technical process. People need to be convinced to make these changes and that the implementation of FAIR will generate a long-term return on investment. Although the implementation of FAIR Guidelines is not straightforward, making our resources FAIR is essential to achieving better science together.

Building Expertise on FAIR Through Evolving Bring Your Own Data(BYOD) Workshops: Describing the Data, Software, and Management-focused Approaches and Their Evolution

Since 2014,"Bring Your Own Data"workshops(BYODs)have been organised to inform people about the process and benefits of making resources Findable,Accessible,Interoperable,and Reusable(FAIR,and the FAIRification process).The BYOD workshops'content and format differ depending on their goal,context,and the background and needs of participants.Data-focused BYODs educate domain experts on how to make their data FAIR to find new answers to research questions.Management-focused BYODs promote the benefits of making data FAIR and instruct project managers and policy-makers on the characteristics of FAIRification projects.Software-focused BYODs gather software developers and experts on FAIR to implement or improve software resources that are used to support FAIRification.Overall,these BYODs intend to foster collaboration between different types of stakeholders involved in data management,curation,and reuse(e.g.domain experts,trainers,developers,data owners,data analysts,FAIR experts).The BYODs also serve as an opportunity to learn what kind of support for FAIRification is needed from different communities and to develop teaching materials based on practical examples and experience.In this paper,we detail the three different structures of the BYODs and describe examples of early BYODs related to plant breeding data,and rare disease registries and biobanks,which have shaped the structure of the workshops.We discuss the latest insights into making BYODs more productive by leveraging our almost ten years of training experience in these workshops,including successes and encountered challenges.Finally,we examine how the participants'feedback has motivated the research on FAIR,including the development of workflows and software.

Exploring the Current Practices,Costs and Benefits of FAIR Implementation in Pharmaceutical Research and Development:A Qualitative Interview Study

The findable,accessible,interoperable,reusable(FAIR)principles for scientific data management and stewardship aim to facilitate data reuse at scale by both humans and machines.Research and development(R&D)in the pharmaceutical industry is becoming increasingly data driven,but managing its data assets according to FAIR principles remains costly and challenging.To date,little scientific evidence exists about how FAIR is currently implemented in practice,what its associated costs and benefits are,and how decisions are made about the retrospective FAIRification of data sets in pharmaceutical R&D.This paper reports the results of semi-structured interviews with 14 pharmaceutical professionals who participate in various stages of drug R&D in seven pharmaceutical businesses.Inductive thematic analysis identified three primary themes of the benefits and costs of FAIRification,and the elements that influence the decision-making process for FAIRifying legacy data sets.Participants collectively acknowledged the potential contribution of FAIRification to data reusability in diverse research domains and the subsequent potential for cost-savings.Implementation costs,however,were still considered a barrier by participants,with the need for considerable expenditure in terms of resources,and cultural change.How decisions were made about FAIRification was influenced by legal and ethical considerations,management commitment,and data prioritisation.The findings have significant implications for those in the pharmaceutical R&D industry who are engaged in driving FAIR implementation,and for external parties who seek to better understand existing practices and challenges.

FAIR Machine Learning Model Pipeline Implementation of COVID-19 Data

Research and development are gradually becoming data-driven and the implementation of the FAIR Guidelines(that data should be Findable, Accessible, Interoperable, and Reusable) for scientific data administration and stewardship has the potential to remarkably enhance the framework for the reuse of research data. In this way, FAIR is aiding digital transformation. The ‘FAIRification’ of data increases the interoperability and(re)usability of data, so that new and robust analytical tools, such as machine learning(ML) models, can access the data to deduce meaningful insights, extract actionable information, and identify hidden patterns. This article aims to build a FAIR ML model pipeline using the generic FAIRification workflow to make the whole ML analytics process FAIR. Accordingly, FAIR input data was modelled using a FAIR ML model. The output data from the FAIR ML model was also made FAIR. For this, a hybrid hierarchical k-means (HHK) clustering ML algorithm was applied to group the data into homogeneous subgroups and ascertain the underlying structure of the data using a Nigerian-based FAIR dataset that contains data on economic factors, healthcare facilities, and coronavirus occurrences in all the 36 states of Nigeria. The model showed that research data and the ML pipeline can be FAIRified, shared, and reused by following the proposed FAIRification workflow and implementing technical architecture.